Zane Lim


Drank
import pandas as pd
import numpy as np
import lime
from lime import lime_tabular
import xgboost as xgb
import matplotlib.pyplot as plt
%matplotlib inline
import sklearn
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.datasets import load_wine
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly.graph_objs as go
init_notebook_mode(connected=True)
print 'pandas version: {}'.format(pd.__version__)
print 'numpy version: {}'.format(np.__version__)
print 'xgboost version: {}'.format(xgb.__version__)
print 'sklearn version: {}'.format(sklearn.__version__)
pandas version: 0.20.3 numpy version: 1.13.1 xgboost version: 0.6 sklearn version: 0.19.0
data = load_wine()
data['data'] = data.data[(data.target != 0)]
data['target'] = data.target[(data.target != 0)]-1
data['target_names'] = ['class_1', 'class_2']
print data.DESCR
Wine Data Database
====================
Notes
-----
Data Set Characteristics:
:Number of Instances: 178 (50 in each of three classes)
:Number of Attributes: 13 numeric, predictive attributes and the class
:Attribute Information:
- 1) Alcohol
- 2) Malic acid
- 3) Ash
- 4) Alcalinity of ash
- 5) Magnesium
- 6) Total phenols
- 7) Flavanoids
- 8) Nonflavanoid phenols
- 9) Proanthocyanins
- 10)Color intensity
- 11)Hue
- 12)OD280/OD315 of diluted wines
- 13)Proline
- class:
- class_0
- class_1
- class_2
:Summary Statistics:
============================= ==== ===== ======= =====
Min Max Mean SD
============================= ==== ===== ======= =====
Alcohol: 11.0 14.8 13.0 0.8
Malic Acid: 0.74 5.80 2.34 1.12
Ash: 1.36 3.23 2.36 0.27
Alcalinity of Ash: 10.6 30.0 19.5 3.3
Magnesium: 70.0 162.0 99.7 14.3
Total Phenols: 0.98 3.88 2.29 0.63
Flavanoids: 0.34 5.08 2.03 1.00
Nonflavanoid Phenols: 0.13 0.66 0.36 0.12
Proanthocyanins: 0.41 3.58 1.59 0.57
Colour Intensity: 1.3 13.0 5.1 2.3
Hue: 0.48 1.71 0.96 0.23
OD280/OD315 of diluted wines: 1.27 4.00 2.61 0.71
Proline: 278 1680 746 315
============================= ==== ===== ======= =====
:Missing Attribute Values: None
:Class Distribution: class_0 (59), class_1 (71), class_2 (48)
:Creator: R.A. Fisher
:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
:Date: July, 1988
This is a copy of UCI ML Wine recognition datasets.
https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data
The data is the results of a chemical analysis of wines grown in the same
region in Italy by three different cultivators. There are thirteen different
measurements taken for different constituents found in the three types of
wine.
Original Owners:
Forina, M. et al, PARVUS -
An Extendible Package for Data Exploration, Classification and Correlation.
Institute of Pharmaceutical and Food Analysis and Technologies,
Via Brigata Salerno, 16147 Genoa, Italy.
Citation:
Lichman, M. (2013). UCI Machine Learning Repository
[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
School of Information and Computer Science.
References
----------
(1)
S. Aeberhard, D. Coomans and O. de Vel,
Comparison of Classifiers in High Dimensional Settings,
Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
Mathematics and Statistics, James Cook University of North Queensland.
(Also submitted to Technometrics).
The data was used with many others for comparing various
classifiers. The classes are separable, though only RDA
has achieved 100% correct classification.
(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
(All results using the leave-one-out technique)
(2)
S. Aeberhard, D. Coomans and O. de Vel,
"THE CLASSIFICATION PERFORMANCE OF RDA"
Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
Mathematics and Statistics, James Cook University of North Queensland.
(Also submitted to Journal of Chemometrics).
data.target_names
['class_1', 'class_2']
data.feature_names
['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']
data.data.shape
(119, 13)
pd.Series(data.target).value_counts()
0 71 1 48 dtype: int64
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3, random_state=0)
print X_train.shape, X_test.shape
(83, 13) (36, 13)
dtrain = xgb.DMatrix(X_train, y_train, feature_names=data.feature_names)
params = {'learning_rate': 0.1, 'max_depth': 4, 'subsample': 1.0, 'colsample_bytree': 0.8,
'objective': 'binary:logistic', 'eval_metric': 'logloss'}
cv = xgb.cv(params, dtrain, 200, nfold=5, stratified=True, early_stopping_rounds=20, seed=0)
cv.iloc[-1]
test-logloss-mean 0.072455 test-logloss-std 0.020414 train-logloss-mean 0.031961 train-logloss-std 0.000370 Name: 61, dtype: float64
params['n_estimators'] = 62
params['n_jobs'] = 5
params['random_state'] = 0
xgb_clf = xgb.XGBClassifier(**params)
xgb_clf.fit(X_train, y_train)
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bytree=0.8, eval_metric='logloss', gamma=0,
learning_rate=0.1, max_delta_step=0, max_depth=4,
min_child_weight=1, missing=None, n_estimators=62, n_jobs=5,
nthread=None, objective='binary:logistic', random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
silent=True, subsample=1.0)
y_probs = xgb_clf.predict_proba(X_test)
y_preds = xgb_clf.predict(X_test)
print classification_report(y_test, y_preds, target_names=data.target_names)
precision recall f1-score support
class_1 1.00 0.91 0.95 23
class_2 0.87 1.00 0.93 13
avg / total 0.95 0.94 0.95 36
xgb.to_graphviz(xgb_clf_2, num_trees=0, size='100,150')
features_importance = pd.DataFrame.from_dict(xgb_clf_2.get_fscore(), orient='index').reset_index().rename(columns={'index': 'features', 0: 'importance'})
features_importance.sort_values('importance', ascending=False, inplace=True)
features_importance.plot.barh(x='features', y='importance', figsize=(15,7))
plt.gca().invert_yaxis()
df_plot = pd.read_csv("./data/pdp/color_int.csv")
iplot(plotly_figure(df_plot, 'Color Intensity', 'Color Intensity',
'Probability of Class 2', 'color_intensity', "yhat"),
filename="figure")
df_plot = pd.read_csv("./data/pdp/flavanoids.csv")
iplot(plotly_figure(df_plot, 'Flavanoids', 'Flavanoids',
'Probability of Class 2', 'flavanoids', "yhat"),
filename="figure")

explainer = lime_tabular.LimeTabularExplainer(
X_train,
feature_names=data.feature_names,
class_names=data.target_names,
discretize_continuous=True
)
i = np.random.randint(0, X_test.shape[0])
print i
test_instance = X_test[i].copy()
0
exp = explainer.explain_instance(test_instance, xgb_clf.predict_proba, num_features=5)
exp.show_in_notebook(show_table=True, show_all=False)
i = np.random.randint(0, X_test.shape[0])
print i
test_instance = X_test[i].copy()
7
exp = explainer.explain_instance(test_instance, xgb_clf.predict_proba, num_features=5)
exp.show_in_notebook(show_table=True, show_all=False)
i = np.random.randint(0, X_test.shape[0])
print i
test_instance = X_test[i].copy()
18
exp = explainer.explain_instance(test_instance, xgb_clf.predict_proba, num_features=5)
exp.show_in_notebook(show_table=True, show_all=False)
test_instance[9] = 2.5
exp = explainer.explain_instance(test_instance, xgb_clf.predict_proba, num_features=5)
exp.show_in_notebook(show_table=True, show_all=False)
test_instance = X_test[i].copy()
test_instance[6] = 1.9
exp = explainer.explain_instance(test_instance, xgb_clf.predict_proba, num_features=5)
exp.show_in_notebook(show_table=True, show_all=False)
Dataset used: 20 newsgroups
http://qwone.com/~jason/20Newsgroups/
The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, across 20 different newsgroups.
from sklearn.datasets import fetch_20newsgroups
newsgroups_train = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))
newsgroups_test = fetch_20newsgroups(subset='test',remove=('headers', 'footers', 'quotes'))
# making class names shorter
class_names = [x.split('.')[-1] if 'misc' not in x else '.'.join(x.split('.')[-2:]) for x in newsgroups_train.target_names]
class_names[3] = 'pc.hardware'
class_names[4] = 'mac.hardware'
Downloading 20news dataset. This may take a few minutes. Downloading dataset from https://ndownloader.figshare.com/files/5975967 (14 MB)
print(','.join(class_names))
atheism,graphics,ms-windows.misc,pc.hardware,mac.hardware,x,misc.forsale,autos,motorcycles,baseball,hockey,crypt,electronics,med,space,christian,guns,mideast,politics.misc,religion.misc
from sklearn.naive_bayes import MultinomialNB
from lime import lime_text
from sklearn.pipeline import make_pipeline
from lime.lime_text import LimeTextExplainer
train_vectors = vectorizer.fit_transform(newsgroups_train.data)
test_vectors = vectorizer.transform(newsgroups_test.data)
nb = MultinomialNB(alpha=.01)
nb.fit(train_vectors, newsgroups_train.target)
c = make_pipeline(vectorizer, nb)
explainer = LimeTextExplainer(class_names=class_names)
exp = explainer.explain_instance(newsgroups_test.data[idx], c.predict_proba, num_features=6, top_labels=2)
print(exp.available_labels())
exp.show_in_notebook(text=newsgroups_test.data[idx])
[8, 7]
exp = explainer.explain_instance(newsgroups_test.data[idx], c.predict_proba, num_features=6, top_labels=2)
print(exp.available_labels())
exp.show_in_notebook(text=newsgroups_test.data[idx])
[17, 15]
exp = explainer.explain_instance(newsgroups_test.data[idx], c.predict_proba, num_features=6, top_labels=2)
print(exp.available_labels())
exp.show_in_notebook(text=newsgroups_test.data[idx])
[12, 1]
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